Every model has a story, every story has a model
GLOBAL AND SOCIAL CHALLENGES
We seek to understand some of the complex issues that impact our world. Specifically, we examine the effects that large and fast migration flows have in cities' and countries' society, governance, and infrastructure; how informal settlements can be absorptive spaces for marginalized communities; and how news media play a role in our attitudes towards humanitarian crises, among other topics.
Research Areas' Focus
1
Urban Resilience and Informal Settlements
Explores how cities adapt to shocks while providing support for marginalized communities.
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Studies the role of informal settlements in providing housing and livelihoods.
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Examines urban infrastructure capacity under environmental, economic, and social stress.
2
Governance Amid Societal Shifts
Analyzes how governments respond to rapid social, technological, and demographic changes.
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Investigates institutional adaptability and policy responses.
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Evaluates trust, legitimacy, and public engagement in changing contexts.
3
Media and Humanitarian Perceptions
Studies how media shape public understanding and response to crises.
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Examines framing, narratives, and emotional impact in media coverage.
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Analyzes the role of information flow in humanitarian aid and policy decisions.
Research Papers and Blogs
Where are they headed next? Modeling emergent displaced camps in the DRC using agent-based models.
Insights:
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The paper presents a prototype agent-based model designed to simulate the formation of spontaneous settlements by internally displaced persons (IDPs) in the Democratic Republic of the Congo, accounting for geographic and social factors that influence IDPs' movement and settlement decisions.
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The model provides insights for humanitarian organizations by predicting where self-settlements may emerge, helping aid agencies locate and deliver assistance to displaced populations who may otherwise remain hidden from formal humanitarian response efforts.
Citation:
E. Frydenlund, P. Foytik, J. J. Padilla and A. Ouattara, "Where are they headed next? Modeling emergent displaced camps in the DRC using agent-based models," 2018 Winter Simulation Conference (WSC), Gothenburg, Sweden, 2018, pp. 22-32, doi: 10.1109/WSC.2018.8632555.
Modeling and Simulation as a Bridge to Advance Practical and Theoretical Insights About Forced Migration Studies
Insights:
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The paper discusses how modeling and simulation (M&S), though underutilized in forced migration studies, can serve as a valuable tool for both policymakers and researchers by translating qualitative findings into dynamic models that explore human movement, humanitarian logistics, health, and other policy-relevant topics.
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M&S enables scenario testing, theory evaluation, integration of diverse data sources, and communication of complex dynamics to policymakers, while highlighting challenges in interdisciplinary collaboration and advocating for broader involvement of migration scholars in model design to improve both policy planning and theoretical development.
Citation:
Frydenlund, E. (2021). Modeling and Simulation as a Bridge to Advance Practical and Theoretical Insights About Forced Migration Studies. Journal on Migration and Human Security, 9(3), 165-181. https://doi.org/10.1177/23315024211035771 (Original work published 2021)
You Are What You Tweet: Connecting the Geographic Variation in America’s Obesity Rate to Twitter Content
Insights:
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The paper investigates the relationship between urban obesity rates and expressions of happiness, diet, and physical activity on social media by analyzing over 200 million geo-tagged tweets from 2012–2013.
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The study finds that urban areas with lower obesity rates tend to have happier tweets, more frequent mentions of healthy foods (like fruits and vegetables), and more discussions of physical activity, suggesting that social media content can provide real-time, population-level indicators related to obesity.
Citation:
Gore RJ, Diallo S, Padilla J (2015) You Are What You Tweet: Connecting the Geographic Variation in America’s Obesity Rate to Twitter Content. PLoS ONE 10(9): e0133505. https://doi.org/10.1371/journal.pone.0133505